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深度学习增强的3D实时光声成像使用从波动成像获得的实验基础真相
Ivana Falco1, Guillaume Godefroy2, Maxime Henry3
1Université Grenoble Alpes, CNRS, LIPhy, Grenoble 38058, France.
Physics in medicine and biology
|October 3, 2025
概括
深度学习通过使用实验数据来提高可见性和对比度来增强3D光声学 (PA) 成像. 这种方法显示了在各种应用中实现实时,无工件的3D PA成像的潜力.
科学领域:
- 生物医学光学 生物医学光学
- 医学成像医学成像
- 医学中的人工智能
背景情况:
- 由于传感器的限制和稀疏的阵列,3D光声学 (PA) 成像面临可见性工件.
- PA波动成像 (PAFI) 提高了可见性,但牺牲了时间分辨率.
- 深度学习 (DL) 对PA图像增强有希望,但需要实验性训练数据.
研究的目的:
- 开发一种基于DL的方法,使用实验数据增强3DPA图像.
- 使用单次拍摄的3D PA图像和PAFI图像来训练一个3D ResU-Net网络.
- 为了评估DL-PAFI网络实时,无工件的3D PA成像的性能.
主要方法:
- 使用一次性3DPA和PAFI图像从胚胎血管系统创建了一个实验性训练数据集.
- 在实验数据集上训练了一个3D ResU-Net神经网络.
- 在新的实验测试图像上评估了网络的性能,并展示了实时染能力.
- 测试了网络在小鼠中预测*in vivo*图像的能力.
主要成果:
- DL-PAFI网络有效地改善了3D PA图像中的可见性和对比度.
- 输出图像分辨率低于PAFI,但仅用实验数据进行训练就产生了良好的表现.
- 使用模拟数据进行预训练进一步提高了整体准确性.
- 在小鼠实时染和初步*in vivo*预测的可行性已被证明.
结论:
- 使用实验数据进行基于DL的增强可以显著提高3D PA成像质量.
- 训练有素的网络显示了实时,无工件的3D PA成像与稀疏阵列的潜力.
- 该方法可适应各种*in vivo*应用,包括跨物种预测.
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